Leveraging Deep Learning Methods for Detecting Deepfake Speeches
Bibliographic record
Abstract
Synthetic speeches, infamously known as Audio Deepfakes (AD), can easily become a menacing tool if they fall into the wrong hands. Moreover, social networks, which are highly vulnerable to deepfake attacks, can potentially cause social chaos. In order to tackle any potential harm, the misuse of deepfakes needs to be prevented. Especially for deepfake audio, detection methods are crucial to tackling the spread of deepfake speeches. In this study, we proposed a deep learning (DL) framework, the Convolutional Neural Network (CNN), to detect deepfake Bengali speeches. Through this study, we contributed to the resolution of certain research gaps, such as the limited number of dedicated researches and the scarcity of Bengali audio datasets comprising the Bengali domain in this field. The proposed model was applied on a set of primary self-created Bengali audio data. As a result, the CNN framework achieved the highest score of 98.24%, compared to a one-dimensional representation foundational CNN model.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".